Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations4943
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory463.5 KiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
annualHomeownersInsurance is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
bathrooms is highly overall correlated with annualHomeownersInsurance and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with annualHomeownersInsurance and 3 other fieldsHigh correlation
homeType_CONDO is highly overall correlated with annualHomeownersInsurance and 2 other fieldsHigh correlation
homeType_OTHER is highly overall correlated with homeType_SINGLE_FAMILYHigh correlation
homeType_SINGLE_FAMILY is highly overall correlated with annualHomeownersInsurance and 3 other fieldsHigh correlation
livingArea is highly overall correlated with annualHomeownersInsurance and 3 other fieldsHigh correlation
price is highly overall correlated with annualHomeownersInsurance and 5 other fieldsHigh correlation
homeType_OTHER is highly imbalanced (54.5%) Imbalance
monthlyHoaFee is highly skewed (γ1 = 66.59234387) Skewed
monthlyHoaFee has 3954 (80.0%) zeros Zeros

Reproduction

Analysis started2024-11-26 21:22:13.563648
Analysis finished2024-11-26 21:22:37.097402
Duration23.53 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

Distinct3695
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-148.97898
Minimum-150.01093
Maximum-70.4831
Zeros0
Zeros (%)0.0%
Negative4943
Negative (%)100.0%
Memory size38.7 KiB
2024-11-26T23:22:37.405711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-150.01093
5-th percentile-149.9591
Q1-149.92893
median-149.87282
Q3-149.81335
95-th percentile-149.72786
Maximum-70.4831
Range79.52783
Interquartile range (IQR)0.115585

Descriptive statistics

Standard deviation7.6134825
Coefficient of variation (CV)-0.051104409
Kurtosis75.159795
Mean-148.97898
Median Absolute Deviation (MAD)0.05776
Skewness8.693605
Sum-736403.08
Variance57.965116
MonotonicityNot monotonic
2024-11-26T23:22:37.985981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-149.73212 13
 
0.3%
-149.94147 13
 
0.3%
-149.89989 12
 
0.2%
-149.92009 12
 
0.2%
-149.72975 11
 
0.2%
-149.82962 10
 
0.2%
-149.88034 8
 
0.2%
-149.89291 8
 
0.2%
-149.89194 7
 
0.1%
-149.94337 7
 
0.1%
Other values (3685) 4842
98.0%
ValueCountFrequency (%)
-150.01093 1
< 0.1%
-150.0097 1
< 0.1%
-150.00902 1
< 0.1%
-150.00879 1
< 0.1%
-150.00493 1
< 0.1%
-150.0028 1
< 0.1%
-150.00247 1
< 0.1%
-150.00201 1
< 0.1%
-150.00186 1
< 0.1%
-150.00182 1
< 0.1%
ValueCountFrequency (%)
-70.4831 1
< 0.1%
-70.483406 2
< 0.1%
-71.42977 1
< 0.1%
-72.25583 1
< 0.1%
-73.088585 1
< 0.1%
-73.171455 1
< 0.1%
-73.702896 1
< 0.1%
-73.819725 1
< 0.1%
-73.860085 1
< 0.1%
-73.893456 1
< 0.1%

monthlyHoaFee
Real number (ℝ)

Skewed  Zeros 

Distinct213
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.672871
Minimum0
Maximum45929
Zeros3954
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:38.305650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile348
Maximum45929
Range45929
Interquartile range (IQR)0

Descriptive statistics

Standard deviation664.56376
Coefficient of variation (CV)11.522987
Kurtosis4595.8062
Mean57.672871
Median Absolute Deviation (MAD)0
Skewness66.592344
Sum285077
Variance441644.99
MonotonicityNot monotonic
2024-11-26T23:22:38.721011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3954
80.0%
18 29
 
0.6%
25 28
 
0.6%
90 24
 
0.5%
300 23
 
0.5%
400 21
 
0.4%
115 20
 
0.4%
265 19
 
0.4%
272 17
 
0.3%
338 16
 
0.3%
Other values (203) 792
 
16.0%
ValueCountFrequency (%)
0 3954
80.0%
4 13
 
0.3%
6 5
 
0.1%
8 5
 
0.1%
10 2
 
< 0.1%
11 6
 
0.1%
12 14
 
0.3%
13 12
 
0.2%
15 11
 
0.2%
16 5
 
0.1%
ValueCountFrequency (%)
45929 1
 
< 0.1%
1307 4
0.1%
1191 1
 
< 0.1%
1114 1
 
< 0.1%
873 2
 
< 0.1%
830 1
 
< 0.1%
752 4
0.1%
691 1
 
< 0.1%
681 6
0.1%
664 1
 
< 0.1%

annualHomeownersInsurance
Real number (ℝ)

High correlation 

Distinct2074
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1681.3073
Minimum5
Maximum11550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:39.176975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile660.1
Q11245
median1630
Q31972
95-th percentile2894.4
Maximum11550
Range11545
Interquartile range (IQR)727

Descriptive statistics

Standard deviation740.1802
Coefficient of variation (CV)0.44024088
Kurtosis13.627176
Mean1681.3073
Median Absolute Deviation (MAD)362
Skewness2.1059821
Sum8310702
Variance547866.74
MonotonicityNot monotonic
2024-11-26T23:22:40.177799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1630 14
 
0.3%
1733 12
 
0.2%
1633 12
 
0.2%
1743 11
 
0.2%
1691 11
 
0.2%
1341 11
 
0.2%
1677 10
 
0.2%
1696 10
 
0.2%
1762 9
 
0.2%
1310 9
 
0.2%
Other values (2064) 4834
97.8%
ValueCountFrequency (%)
5 1
< 0.1%
252 1
< 0.1%
335 1
< 0.1%
366 1
< 0.1%
378 2
< 0.1%
382 1
< 0.1%
384 1
< 0.1%
388 1
< 0.1%
394 1
< 0.1%
397 1
< 0.1%
ValueCountFrequency (%)
11550 1
< 0.1%
8646 1
< 0.1%
8004 1
< 0.1%
7710 1
< 0.1%
6688 1
< 0.1%
6539 1
< 0.1%
6274 1
< 0.1%
6273 1
< 0.1%
6143 1
< 0.1%
6071 1
< 0.1%

livingArea
Real number (ℝ)

High correlation 

Distinct1860
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1812.9887
Minimum1
Maximum14500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:40.484483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile744
Q11171.5
median1716
Q32147.5
95-th percentile3511.8
Maximum14500
Range14499
Interquartile range (IQR)976

Descriptive statistics

Standard deviation907.71194
Coefficient of variation (CV)0.5006716
Kurtosis16.452872
Mean1812.9887
Median Absolute Deviation (MAD)500
Skewness2.3742231
Sum8961603
Variance823940.97
MonotonicityNot monotonic
2024-11-26T23:22:41.093720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1040 67
 
1.4%
1920 51
 
1.0%
1824 47
 
1.0%
1872 43
 
0.9%
1728 39
 
0.8%
1976 32
 
0.6%
1200 31
 
0.6%
988 27
 
0.5%
1152 26
 
0.5%
1500 23
 
0.5%
Other values (1850) 4557
92.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
20 1
< 0.1%
320 1
< 0.1%
399 2
< 0.1%
400 1
< 0.1%
404 1
< 0.1%
415 1
< 0.1%
446 1
< 0.1%
450 1
< 0.1%
ValueCountFrequency (%)
14500 1
< 0.1%
13244 1
< 0.1%
8349 1
< 0.1%
7500 1
< 0.1%
7227 1
< 0.1%
7010 2
< 0.1%
7004 1
< 0.1%
6984 1
< 0.1%
6878 1
< 0.1%
6692 1
< 0.1%

zipcode
Real number (ℝ)

Distinct75
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98716.434
Minimum2649
Maximum99518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:41.491383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2649
5-th percentile99501
Q199502
median99507
Q399515
95-th percentile99518
Maximum99518
Range96869
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7461.2715
Coefficient of variation (CV)0.075582871
Kurtosis107.03132
Mean98716.434
Median Absolute Deviation (MAD)5
Skewness-10.18448
Sum4.8795533 × 108
Variance55670572
MonotonicityNot monotonic
2024-11-26T23:22:41.754621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99507 900
18.2%
99502 838
17.0%
99508 737
14.9%
99517 607
12.3%
99504 405
8.2%
99518 396
8.0%
99501 348
 
7.0%
99515 248
 
5.0%
99503 240
 
4.9%
99516 156
 
3.2%
Other values (65) 68
 
1.4%
ValueCountFrequency (%)
2649 3
0.1%
2865 1
 
< 0.1%
6401 1
 
< 0.1%
6415 1
 
< 0.1%
6607 1
 
< 0.1%
8054 1
 
< 0.1%
8088 1
 
< 0.1%
8332 1
 
< 0.1%
10704 1
 
< 0.1%
11355 1
 
< 0.1%
ValueCountFrequency (%)
99518 396
8.0%
99517 607
12.3%
99516 156
 
3.2%
99515 248
 
5.0%
99508 737
14.9%
99507 900
18.2%
99504 405
8.2%
99503 240
 
4.9%
99502 838
17.0%
99501 348
 
7.0%

propertyTaxRate
Real number (ℝ)

Distinct56
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3070362
Minimum0
Maximum2.43
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:42.038114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.31
Q11.31
median1.31
Q31.31
95-th percentile1.31
Maximum2.43
Range2.43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.070111319
Coefficient of variation (CV)0.053641451
Kurtosis157.39508
Mean1.3070362
Median Absolute Deviation (MAD)0
Skewness-5.6114206
Sum6460.68
Variance0.004915597
MonotonicityNot monotonic
2024-11-26T23:22:42.348171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.31 4874
98.6%
0.63 3
 
0.1%
0.59 2
 
< 0.1%
0.66 2
 
< 0.1%
1.14 2
 
< 0.1%
0.91 2
 
< 0.1%
1.36 2
 
< 0.1%
0 2
 
< 0.1%
0.31 2
 
< 0.1%
0.41 2
 
< 0.1%
Other values (46) 50
 
1.0%
ValueCountFrequency (%)
0 2
< 0.1%
0.31 2
< 0.1%
0.41 2
< 0.1%
0.42 2
< 0.1%
0.45 1
 
< 0.1%
0.57 1
 
< 0.1%
0.59 2
< 0.1%
0.61 1
 
< 0.1%
0.62 1
 
< 0.1%
0.63 3
0.1%
ValueCountFrequency (%)
2.43 1
< 0.1%
2.35 1
< 0.1%
2.22 2
< 0.1%
2.13 1
< 0.1%
1.89 1
< 0.1%
1.87 1
< 0.1%
1.85 1
< 0.1%
1.72 1
< 0.1%
1.69 1
< 0.1%
1.66 1
< 0.1%

bathrooms
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0984422
Minimum0
Maximum30
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:42.564552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2
Q32.5
95-th percentile3.5
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.97919505
Coefficient of variation (CV)0.4666295
Kurtosis162.74267
Mean2.0984422
Median Absolute Deviation (MAD)0.5
Skewness6.8613747
Sum10372.6
Variance0.95882295
MonotonicityNot monotonic
2024-11-26T23:22:42.747098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 1960
39.7%
1 932
18.9%
3 673
 
13.6%
2.5 580
 
11.7%
1.5 417
 
8.4%
4 133
 
2.7%
3.5 95
 
1.9%
5 32
 
0.6%
4.5 24
 
0.5%
0 24
 
0.5%
Other values (15) 73
 
1.5%
ValueCountFrequency (%)
0 24
 
0.5%
0.5 10
 
0.2%
1 932
18.9%
1.3 1
 
< 0.1%
1.5 417
 
8.4%
1.75 15
 
0.3%
1.8 1
 
< 0.1%
2 1960
39.7%
2.25 3
 
0.1%
2.5 580
 
11.7%
ValueCountFrequency (%)
30 1
 
< 0.1%
21 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6.5 1
 
< 0.1%
6 11
 
0.2%
5.5 8
 
0.2%
5 32
0.6%
4.5 24
0.5%

bedrooms
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2055432
Minimum0
Maximum30
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:42.915959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2507519
Coefficient of variation (CV)0.39018409
Kurtosis54.123993
Mean3.2055432
Median Absolute Deviation (MAD)1
Skewness3.5497933
Sum15845
Variance1.5643804
MonotonicityNot monotonic
2024-11-26T23:22:43.102543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2091
42.3%
4 1203
24.3%
2 1000
20.2%
5 245
 
5.0%
1 200
 
4.0%
6 138
 
2.8%
7 25
 
0.5%
8 14
 
0.3%
10 11
 
0.2%
0 8
 
0.2%
Other values (4) 8
 
0.2%
ValueCountFrequency (%)
0 8
 
0.2%
1 200
 
4.0%
2 1000
20.2%
3 2091
42.3%
4 1203
24.3%
5 245
 
5.0%
6 138
 
2.8%
7 25
 
0.5%
8 14
 
0.3%
9 5
 
0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
21 1
 
< 0.1%
14 1
 
< 0.1%
10 11
 
0.2%
9 5
 
0.1%
8 14
 
0.3%
7 25
 
0.5%
6 138
 
2.8%
5 245
 
5.0%
4 1203
24.3%

price
Real number (ℝ)

High correlation 

Distinct3196
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400306.77
Minimum1250
Maximum2750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.7 KiB
2024-11-26T23:22:43.461152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile157120
Q1296400
median388200
Q3469550
95-th percentile689240
Maximum2750000
Range2748750
Interquartile range (IQR)173150

Descriptive statistics

Standard deviation176233.14
Coefficient of variation (CV)0.44024522
Kurtosis13.626887
Mean400306.77
Median Absolute Deviation (MAD)86200
Skewness2.105954
Sum1.9787164 × 109
Variance3.1058121 × 1010
MonotonicityNot monotonic
2024-11-26T23:22:44.237616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388100 7
 
0.1%
420900 6
 
0.1%
375900 6
 
0.1%
419500 6
 
0.1%
403900 6
 
0.1%
462800 6
 
0.1%
394000 6
 
0.1%
411600 6
 
0.1%
417600 5
 
0.1%
412600 5
 
0.1%
Other values (3186) 4884
98.8%
ValueCountFrequency (%)
1250 1
< 0.1%
60000 1
< 0.1%
79700 1
< 0.1%
87100 1
< 0.1%
90100 2
< 0.1%
91000 1
< 0.1%
91500 1
< 0.1%
92400 1
< 0.1%
93800 1
< 0.1%
94600 1
< 0.1%
ValueCountFrequency (%)
2750000 1
< 0.1%
2058500 1
< 0.1%
1905800 1
< 0.1%
1835600 1
< 0.1%
1592300 1
< 0.1%
1556900 1
< 0.1%
1493700 1
< 0.1%
1493600 1
< 0.1%
1462700 1
< 0.1%
1445400 1
< 0.1%

homeType_CONDO
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4192 
1
751 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Length

2024-11-26T23:22:44.531278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T23:22:44.714115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4192
84.8%
1 751
 
15.2%

homeType_SINGLE_FAMILY
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
1
3613 
0
1330 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Length

2024-11-26T23:22:44.888428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T23:22:45.125595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring characters

ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3613
73.1%
0 1330
 
26.9%

homeType_OTHER
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
0
4470 
1
473 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4943
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4470
90.4%
1 473
 
9.6%

Length

2024-11-26T23:22:45.521694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T23:22:45.732480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4470
90.4%
1 473
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 4470
90.4%
1 473
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4470
90.4%
1 473
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4470
90.4%
1 473
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4943
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4470
90.4%
1 473
 
9.6%

Interactions

2024-11-26T23:22:31.116938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:14.295074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:16.531899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:18.263639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:19.919183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:21.772084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:23.602415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:26.183323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:28.284897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:31.661032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:14.477595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:16.743013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:18.474362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:20.110847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:21.970755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:24.206260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:26.429214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:28.680342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:32.001841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:14.626290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:16.895658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:18.696977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:20.274165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:22.137627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:24.514936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:26.642497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:28.929779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:32.681425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:14.816807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:17.060126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:18.875694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:20.491910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:22.340844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:24.816019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:26.858100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:29.158922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:33.470467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:15.044402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:17.248070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:19.046332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:20.720235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:22.544591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:25.108121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:27.083371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:29.443682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:33.904050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:15.248236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:17.427070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:19.197341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:20.928252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:22.752237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:25.319443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:27.288187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:29.676456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:34.302590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:15.548014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:17.625719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:19.410165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:21.140250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:22.958249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:25.517763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:27.506968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:30.017743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:34.862669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:15.815374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:17.840619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:19.584909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:21.346695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:23.164077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:25.720144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:27.738782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:30.292794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:35.299930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:16.049142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:18.085029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:19.735257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:21.561404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:23.377361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:25.926188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:27.992891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-26T23:22:30.625125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-26T23:22:45.887574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
annualHomeownersInsurancebathroomsbedroomshomeType_CONDOhomeType_OTHERhomeType_SINGLE_FAMILYlivingArealongitudemonthlyHoaFeepricepropertyTaxRatezipcode
annualHomeownersInsurance1.0000.7110.6620.6720.1140.5220.879-0.052-0.2571.000-0.001-0.014
bathrooms0.7111.0000.5920.0860.2110.0560.7550.007-0.0600.711-0.0130.024
bedrooms0.6620.5921.0000.2900.3670.1640.7290.027-0.2780.6620.0360.031
homeType_CONDO0.6720.0860.2901.0000.1360.6970.4500.0000.0000.6710.0400.000
homeType_OTHER0.1140.2110.3670.1361.0000.5350.1870.0650.0000.1140.0740.000
homeType_SINGLE_FAMILY0.5220.0560.1640.6970.5351.0000.3110.0470.0000.5210.0450.011
livingArea0.8790.7550.7290.4500.1870.3111.0000.018-0.2140.8790.016-0.017
longitude-0.0520.0070.0270.0000.0650.0470.0181.000-0.021-0.052-0.0620.001
monthlyHoaFee-0.257-0.060-0.2780.0000.0000.000-0.214-0.0211.000-0.257-0.032-0.011
price1.0000.7110.6620.6710.1140.5210.879-0.052-0.2571.000-0.001-0.014
propertyTaxRate-0.001-0.0130.0360.0400.0740.0450.016-0.062-0.032-0.0011.0000.057
zipcode-0.0140.0240.0310.0000.0000.011-0.0170.001-0.011-0.0140.0571.000

Missing values

2024-11-26T23:22:35.775263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-26T23:22:36.800982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudemonthlyHoaFeeannualHomeownersInsurancelivingAreazipcodepropertyTaxRatebathroomsbedroomspricehomeType_CONDOhomeType_SINGLE_FAMILYhomeType_OTHER
0-149.908070.028402668.0995011.312.03.0676100010
1-149.908220.029343179.0995011.312.03.0698600010
2-149.908330.041873059.0995011.313.04.0996800010
3-149.908340.029201642.0995011.312.05.0695300010
4-149.907490.041004483.0995011.314.04.0976100100
5-149.907230.025352560.0995011.313.53.0603600100
6-149.907230.030423224.0995011.313.06.0724400001
7-149.905460.018652087.0995011.313.02.0444100100
8-149.910570.0862899.0995011.311.02.0205200100
9-149.910370.01944678.0995011.311.01.0462800010
longitudemonthlyHoaFeeannualHomeownersInsurancelivingAreazipcodepropertyTaxRatebathroomsbedroomspricehomeType_CONDOhomeType_SINGLE_FAMILYhomeType_OTHER
4933-149.7784428.023142688.0995161.312.04.0550900010
4934-149.7801728.019331872.0995161.312.03.0460300010
4935-149.7879528.018351789.0995161.312.03.0436800010
4936-149.7887128.018031496.0995161.312.53.0429400010
4937-149.7860628.019161838.0995161.312.03.0456300010
4938-149.7840628.027644263.0995161.312.53.0658100010
4939-149.7829628.021602200.0995161.312.04.0514200010
4940-149.7522045.027684180.0995071.314.55.0659000010
4941-149.7565845.029793928.0995071.313.05.0709400010
4942-149.7573045.024102576.0995071.312.54.0573900010

Duplicate rows

Most frequently occurring

longitudemonthlyHoaFeeannualHomeownersInsurancelivingAreazipcodepropertyTaxRatebathroomsbedroomspricehomeType_CONDOhomeType_SINGLE_FAMILYhomeType_OTHER# duplicates
0-149.79823348.0454821.0995081.311.02.01081001002